Environmental Science
- 17 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
environmental-science is a Claude skill that analyzes environmental and climate data, including trends, pollution, ecological modeling, and carbon footprint.
About
This skill analyzes environmental and climate data, including temperature trends, pollution monitoring, ecological modeling, and carbon-footprint assessment. A developer uses it when working with climate anomalies, air/water quality, or biodiversity metrics. It applies non-parametric trend tests and species distribution models and compares results against regulatory thresholds.
- Analyzes climate, pollution, and biodiversity data with trend statistics
- Applies Mann-Kendall, Sen's slope, and species distribution models
- Computes impact metrics against regulatory thresholds (NAAQS, WHO)
Environmental Science by the numbers
- 17 all-time installs (skills.sh)
- Ranked #1,286 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
environmental-science capabilities & compatibility
Free; draws on open climate and environmental datasets.
- Capabilities
- exploratory data analysis · epidemiology
- Works with
- weather
- Use cases
- data analysis · research
- Pricing
- Free
What environmental-science says it does
Apply trend analysis (Mann-Kendall, Sen's slope for non-parametric trends).
Compare against regulatory thresholds (EPA NAAQS, WHO guidelines).
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| Installs | 17 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Use it to analyze environmental and climate data: temperature trends, pollution, ecological modeling, and carbon footprint.
Who is it for?
Statistical analysis of climate, pollution, and biodiversity data against regulatory baselines.
Skip if: Environmental compliance paperwork or non-analytical sustainability tasks.
When should I use this skill?
The user discusses climate change, ecosystems, pollutants, or sustainability assessments.
What you get
Delivers trend and impact analysis with uncertainty quantified and compared to regulatory standards.
- Trend plots with confidence bands
- Impact metrics vs. regulatory thresholds
- Scenario projections
By the numbers
- 7-step methodology
- 8-item quality checklist
- 6 databases/tools listed
Files
When to Trigger
Activate this skill when the user mentions:
- Climate data, temperature anomalies, CO2 levels, greenhouse gases
- Air/water quality, pollutant concentrations, EPA standards
- Ecological modeling, species distribution, biodiversity indices
- Carbon footprint, life cycle assessment (LCA), emissions inventory
- Remote sensing, satellite imagery for environmental monitoring
- Deforestation, habitat loss, conservation planning
- Ocean acidification, sea level rise, ice sheet dynamics
Step-by-Step Methodology
1. Define the environmental question - Specify the spatial scale (local, regional, global), temporal range, and environmental domain (atmosphere, hydrosphere, lithosphere, biosphere). 2. Data acquisition - Identify appropriate datasets: NOAA/NASA for climate, EPA for pollution, GBIF for biodiversity, Copernicus for satellite data. Check data quality, coverage, and temporal resolution. 3. Exploratory analysis - Visualize spatial and temporal patterns. Plot time series for trends, anomalies, and seasonal decomposition. Map spatial distributions using appropriate projections. 4. Statistical modeling - Apply trend analysis (Mann-Kendall, Sen's slope for non-parametric trends). Use regression models for exposure-response relationships. For ecological data: species distribution models (MaxEnt, random forests), diversity indices (Shannon, Simpson). 5. Impact assessment - Quantify environmental impact using standard metrics: carbon equivalent (tCO2e), air quality index (AQI), water quality index (WQI), ecological footprint. Compare against regulatory thresholds (EPA NAAQS, WHO guidelines). 6. Scenario analysis - Model future projections under different scenarios (RCP/SSP pathways for climate, land-use change scenarios). Conduct sensitivity analysis on key parameters. 7. Communication - Present findings with clear maps, time series, and comparison to baselines. Translate technical results into policy-relevant language.
Key Databases and Tools
- NOAA / NASA GISS - Climate and weather data
- EPA / EEA - Pollution and environmental monitoring
- Copernicus / MODIS - Satellite remote sensing
- GBIF - Global biodiversity occurrence records
- IPCC AR6 - Climate assessment reports and scenarios
- Our World in Data - Environmental statistics
Output Format
- Time series plots with trend lines, confidence bands, and anomaly baselines.
- Maps with proper projections, color scales, and legends (use diverging colormaps for anomalies).
- Impact metrics in standard units with regulatory threshold comparisons.
- Scenario projections clearly labeled with assumptions.
Quality Checklist
- [ ] Data source, spatial resolution, and temporal coverage documented
- [ ] Baseline period defined for anomaly calculations
- [ ] Appropriate statistical tests for trend significance
- [ ] Uncertainty quantified and communicated (confidence intervals, ensemble spread)
- [ ] Regulatory standards cited with specific thresholds
- [ ] Map projection appropriate for the geographic extent
- [ ] Seasonal and cyclical patterns separated from long-term trends
- [ ] Limitations of data coverage and model assumptions stated
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What trend methods does it use?
Mann-Kendall and Sen's slope for non-parametric trend detection, plus seasonal decomposition.
What data sources does it reference?
NOAA/NASA GISS, EPA/EEA, Copernicus/MODIS, GBIF, IPCC AR6, and Our World in Data.